\section{Introduction}
Despite a plethora of sequenced genomes, a paucity of genome-scale
metabolic models have been published thus far.  The bottleneck comes
from the extensive manual effort is currently required to reconstruct
the metabolic network.  In order to be effective, a model of
metabolism should be {\em coherent}, in the sense that the model
should obey physico-chemical constraints, such as charge and mass
balance.  It should be {\em complete}, in the sense that all genes
that code for metabolic enzymes should be part of the model.
Finally, the model should be {\em consistent} with conditional gene
essentiality experiments that measure growth/no-growth under various
different nutrient media and gene knockouts.

Inspired by computer models of skill acquisition\cite{HACKER} and
robot scientists that automate the scientific process\cite{king2004},
we have developed {\tt BIOHACKER} as a network debugging tool to
detect incoherence in the network, generate reasonable hypotheses for
filling in gaps in incomplete networks, and predict sufficient
nutrient sets that are consistent with conditional essentiality
experiments.


